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Machine Learning and Generative AI for Marketing

Machine Learning and Generative AI for Marketing

By : Yoon Hyup Hwang, Nicholas C. Burtch
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Machine Learning and Generative AI for Marketing

Machine Learning and Generative AI for Marketing

By: Yoon Hyup Hwang, Nicholas C. Burtch

Overview of this book

In the dynamic world of marketing, the integration of artificial intelligence (AI) and machine learning (ML) is no longer just an advantage—it's a necessity. Moreover, the rise of generative AI (GenAI) helps with the creation of highly personalized, engaging content that resonates with the target audience. This book provides a comprehensive toolkit for harnessing the power of GenAI to craft marketing strategies that not only predict customer behaviors but also captivate and convert, leading to improved cost per acquisition, boosted conversion rates, and increased net sales. Starting with the basics of Python for data analysis and progressing to sophisticated ML and GenAI models, this book is your comprehensive guide to understanding and applying AI to enhance marketing strategies. Through engaging content & hands-on examples, you'll learn how to harness the capabilities of AI to unlock deep insights into customer behaviors, craft personalized marketing messages, and drive significant business growth. Additionally, you'll explore the ethical implications of AI, ensuring that your marketing strategies are not only effective but also responsible and compliant with current standards By the conclusion of this book, you'll be equipped to design, launch, and manage marketing campaigns that are not only successful but also cutting-edge.
Table of Contents (16 chapters)
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14
Other Books You May Enjoy
15
Index

Summary

This chapter explored the potential of FSL and its promise for refining AI-driven marketing strategies to enhance brand presence. Building on the principles introduced through ZSL, we explored how FSL leverages a limited dataset to enable rapid adaptation of AI models to new tasks. This is crucial in the fast-paced marketing domain, where aligning quickly with evolving consumer preferences and market trends can have a significant impact on a brand’s relevance and engagement.

While FSL focuses on quick adaptability using minimal examples, transfer learning complements this by applying pre-trained models fine-tuned for specific tasks, thereby minimizing the need for extensive retraining. The chapter emphasized practical strategies combining these methodologies to optimize your marketing efforts. Through approaches like the MAML approach, we demonstrated how you can use meta-learning frameworks for marketing.

As we proceed, the next chapter will introduce the concept...

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Machine Learning and Generative AI for Marketing
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